A computer-implemented method and an apparatus for deep learning

A computer-implemented method for deep learning, comprising the following steps:

• obtaining a meta network including a set of incubating modules, wherein each of the set of incubating modules includes at least one basic unit of an architecture of a deep learning network, and the meta network is pre-trained on a dataset;
• independently training, on the dataset, a set of modules with each of the set of modules corresponding to a respective one of the set of incubating modules, by training the meta network with one of the set of incubating modules being substituted by one of the set of modules corresponding to the one of the set of incubating modules on the dataset for training of the one of the set of modules, wherein each module of the set of modules includes more than one basic units of the architecture of the deep learning network;
• assembling the independently trained modules of the set of modules to form an assembled model;
• and
• obtaining, based at least in part on the assembled model, the deep learning network that is optimized on the dataset.

A computer-implemented method of deep learning for a task, comprising the following steps:

• obtaining a meta network including a set of incubating modules, wherein each of the set of incubating modules includes at least one basic unit of an architecture of a deep learning network for the task comprising image or speech recognition, and the meta network is pre-trained on a dataset comprising images or speech signals;
• independently training, on the dataset, a set of modules with each of the set of modules corresponding to a respective one of the set of incubating modules, by training the meta network with one of the set of incubating modules being substituted by one of the set of modules corresponding to the one of the set of incubating modules on the dataset for training of the one of the set of modules, wherein module of the set of modules comprises more than one basic units of the architecture of the deep learning network;
• assembling the independently trained modules of the set of modules to form an assembled model;
• and
• obtaining, based at least in part on the assembled model, the deep learning network that is optimized on the dataset.

An apparatus for deep learning, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to perform a computer-implemented method for deep learning, including the following steps:
• obtaining a meta network including a set of incubating modules, wherein each of the set of incubating modules includes at least one basic unit of an architecture of a deep learning network, and the meta network is pre-trained on a dataset,
• independently training, on the dataset, a set of modules with each of the set of modules corresponding to a respective one of the set of incubating modules, by training the meta network with one of the set of incubating modules being substituted by one of the set of modules corresponding to the one of the set of incubating modules on the dataset for training of the one of the set of modules, wherein each module of the set of modules includes more than one basic units of the architecture of the deep learning network,
• assembling the independently trained modules of the set of modules to form an assembled model, and
• obtaining, based at least in part on the assembled model, the deep learning network that is optimized on the dataset.

A non-transitory computer readable medium on which is stored computer code for deep learning, the computer code when executed by a processor, causing the processor to perform the following steps:

• obtaining a meta network including a set of incubating modules, wherein each of the set of incubating modules includes at least one basic unit of an architecture of a deep learning network, and the meta network is pre-trained on a dataset;
• independently training, on the dataset, a set of modules with each of the set of modules corresponding to a respective one of the set of incubating modules, by training the meta network with one of the set of incubating modules being substituted by one of the set of modules corresponding to the one of the set of incubating modules on the dataset for training of the one of the set of modules, wherein each module of the set of modules includes more than one basic units of the architecture of the deep learning network;
• assembling the independently trained modules of the set of modules to form an assembled model;
• and
• obtaining, based at least in part on the assembled model, the deep learning network that is optimized on the dataset.